Squat and VIM Effects on a Towed Gravity-Based Structure With Low Under Bottom Clearance
Bibliographic record
Abstract
Computational Fluid Dynamics (CFD) are used to predict the behavior of a massive Gravity-Based Structure (GBS) during open-water towing. Generally, it is poorly understood how the hydrodynamic interaction between the shaft (slender portion of the GBS structure at the waterline), free surface and caisson (lower, storage portion of the GBS) influences vortex shedding and potential VIM response. There is also limited ability to quantify the expected squat response of a structure of the substantial mass and bottom profile of a GBS being towed through areas of decreasing under bottom clearance. In order to properly capture all relevant physical effects, the Detached Eddy Simulations (DES) turbulence model is used. The resulting mesh is somewhat coarser and temporal resolution lower than in a previous study [1] which focused on accurate pitch and roll damping estimates. However, new physical decay test data confirm that the current model reproduces the damping in the relevant amplitude range. The planned towing speed results in reduced velocities for the shaft in the range where Vortex Induced Motions (VIM) typically occur. The numerical set-up is qualified by studying the shaft alone in a uniform current; self-sustained VIM is triggered, with behavior similar to known results. Two towing velocities and loading conditions are studied; a higher speed with a large under bottom clearance and a lower speed where the under bottom clearance is just a few meters. When no VIM is simulated for these conditions, a higher speed corresponding to the VIM range for the shaft is studied. Due to the presence of the caisson and the significant total mass of the GBS, no material VIM occurs. This is explained by a simplified one-degree of freedom model combining the excitation force on the shaft with the damping characteristics of the caisson and total GBS added mass and inertia. The simulations are also used to assess if large pitch or roll motions may be triggered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".